A Maximum Likelihood Framework for Integrating Taxonomies

Suju Rajan, Kunal Punera , Joydeep Ghosh

Many approaches have been proposed for the problem of mapping categories (classes)from a source taxonomy to classes in a master taxonomy. Most of these techniques, however, ignore the hierarchical structure of the taxonomies. In this paper, we propose a maximum likelihood based framework which exploits the hierarchical structure to obtain a more natural mapping between the source classes and the master taxonomy. Furthermore, unlike previous work, our technique also inserts source classes into appropriate places of the master hierarchy creating new categories if required. We evaluate our approach on text and hyperspectral datasets.

Content Area: 12. Machine Learning

Subjects: 12. Machine Learning and Discovery; 11.2 Ontologies

Submitted: May 10, 2005


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